2 research outputs found
A New Estimation Algorithm for Box-Cox Transformation Cure Rate Model and Comparison With EM Algorithm
In this paper, we develop a new estimation procedure based on the non-linear
conjugate gradient (NCG) algorithm for the Box-Cox transformation cure rate
model. We compare the performance of the NCG algorithm with the well-known
expectation maximization (EM) algorithm through a simulation study and show the
advantages of the NCG algorithm over the EM algorithm. In particular, we show
that the NCG algorithm allows simultaneous maximization of all model parameters
when the likelihood surface is flat with respect to a Box-Cox model parameter.
This is a big advantage over the EM algorithm, where a profile likelihood
approach has been proposed in the literature that may not provide satisfactory
results. We finally use the NCG algorithm to analyze a well-known melanoma data
and show that it results in a better fit
A New Non-Linear Conjugate Gradient Algorithm for Destructive Cure Rate Model and a Simulation Study: Illustration with Negative Binomial Competing Risks
In this paper, we propose a new estimation methodology based on a projected
non-linear conjugate gradient (PNCG) algorithm with an efficient line search
technique. We develop a general PNCG algorithm for a survival model
incorporating a proportion cure under a competing risks setup, where the
initial number of competing risks are exposed to elimination after an initial
treatment (known as destruction). In the literature, expectation maximization
(EM) algorithm has been widely used for such a model to estimate the model
parameters. Through an extensive Monte Carlo simulation study, we compare the
performance of our proposed PNCG with that of the EM algorithm and show the
advantages of our proposed method. Through simulation, we also show the
advantages of our proposed methodology over other optimization algorithms
(including other conjugate gradient type methods) readily available as R
software packages. To show these we assume the initial number of competing
risks to follow a negative binomial distribution although our general algorithm
allows one to work with any competing risks distribution. Finally, we apply our
proposed algorithm to analyze a well-known melanoma data.Comment: arXiv admin note: text overlap with arXiv:1905.0596